CBSE Textbook: Problem Scoping | Artificial Intelligence (AI) for Class 9 PDF Download

 Page 1


16 
 
1.2 AI Project Cycle 
 
Lesson Title: AI Project Cycle Approach: Interactive Session 
Summary: Students will learn about the AI Project Cycle and get familiar with it. 
Learning Objectives: Students will know how they can get started on an AI project. 
Learning Outcomes: Describe the stages in the AI project cycle. 
Pre-requisites: Basic computer literacy 
Key-concepts: AI project cycle 
 
Let us think! 
? Problem Scoping means 
 
 
? Data Acquisition means 
 
 
? Data Exploration means 
 
 
? Modelling means 
 
 
? Evaluation means 
 
 
Page 2


16 
 
1.2 AI Project Cycle 
 
Lesson Title: AI Project Cycle Approach: Interactive Session 
Summary: Students will learn about the AI Project Cycle and get familiar with it. 
Learning Objectives: Students will know how they can get started on an AI project. 
Learning Outcomes: Describe the stages in the AI project cycle. 
Pre-requisites: Basic computer literacy 
Key-concepts: AI project cycle 
 
Let us think! 
? Problem Scoping means 
 
 
? Data Acquisition means 
 
 
? Data Exploration means 
 
 
? Modelling means 
 
 
? Evaluation means 
 
 
17 
 
Ask students about possible solutions to this problem before moving ahead. 
Invite them to think of non-AI solutions as well. 
? Deployment means 
 
 
 
Let us understand! 
Let us go through the AI project cycle with the help of an example. 
 
Problem: Pest infestation damages crops 
The cotton industry in India consists of 6 million local farmers. Cotton crops frequently get infected with 
the Pink Bollworm. It is difficult to see these insects with the naked eye. Small farmers find it very difficult 
to get rid of these insects. They do not have advanced tools and techniques to protect their plants from 
Pink Bollworm. 
 
Can we solve this problem with AI? How? 
Watch the video at this link - https://www.youtube.com/watch?v=LP_A4jydmz4 
 
Now that you are aware of AI concepts, plan to use them in accomplishing your task. 
Start with listing down all the factors which you need to consider to save the cotton crop. 
This system aims to: 
 
 
 
Page 3


16 
 
1.2 AI Project Cycle 
 
Lesson Title: AI Project Cycle Approach: Interactive Session 
Summary: Students will learn about the AI Project Cycle and get familiar with it. 
Learning Objectives: Students will know how they can get started on an AI project. 
Learning Outcomes: Describe the stages in the AI project cycle. 
Pre-requisites: Basic computer literacy 
Key-concepts: AI project cycle 
 
Let us think! 
? Problem Scoping means 
 
 
? Data Acquisition means 
 
 
? Data Exploration means 
 
 
? Modelling means 
 
 
? Evaluation means 
 
 
17 
 
Ask students about possible solutions to this problem before moving ahead. 
Invite them to think of non-AI solutions as well. 
? Deployment means 
 
 
 
Let us understand! 
Let us go through the AI project cycle with the help of an example. 
 
Problem: Pest infestation damages crops 
The cotton industry in India consists of 6 million local farmers. Cotton crops frequently get infected with 
the Pink Bollworm. It is difficult to see these insects with the naked eye. Small farmers find it very difficult 
to get rid of these insects. They do not have advanced tools and techniques to protect their plants from 
Pink Bollworm. 
 
Can we solve this problem with AI? How? 
Watch the video at this link - https://www.youtube.com/watch?v=LP_A4jydmz4 
 
Now that you are aware of AI concepts, plan to use them in accomplishing your task. 
Start with listing down all the factors which you need to consider to save the cotton crop. 
This system aims to: 
 
 
 
18 
 
 
Now, as you interact with the farmers, you get to know different types of worms affecting 
the cotton crop. You will collect the following data 
? Images of the pest 
? Farmer names 
? Village names 
? Farm size 
? Pesticide usage 
 
After acquiring the required data, you realise that it is not uniform. Some images are small in size while 
others are big. Some images and other data are missing while you have multiple copies of others. So, we 
clean the data, try to make it uniform and fill in the missing data to make it more understandable. 
By exploring the data, researchers can identify patterns and trends related to Pink Bollworm infestations, 
pesticide usage, crop yields, and other relevant factors. 
 
After exploring the data, now you know that you need to develop an AI-enabled app using which the 
farmers will click the pictures of the collected pests using the phone camera. The AI app then decides 
whether the image is valid. Based on the number of pests recognized by the system and rules laid out by 
entomologists, recommendations are displayed 
 
 
Your pest management system is now complete! You test it by first emptying the trap of pests onto a 
blank sheet of paper and opening the app, then clicking pictures of pests. You notice that the results 
were 70% correct. After evaluating this model, you work on other shortlisted AI algorithms and work on 
them. 
You test the algorithms to 
 
 
Page 4


16 
 
1.2 AI Project Cycle 
 
Lesson Title: AI Project Cycle Approach: Interactive Session 
Summary: Students will learn about the AI Project Cycle and get familiar with it. 
Learning Objectives: Students will know how they can get started on an AI project. 
Learning Outcomes: Describe the stages in the AI project cycle. 
Pre-requisites: Basic computer literacy 
Key-concepts: AI project cycle 
 
Let us think! 
? Problem Scoping means 
 
 
? Data Acquisition means 
 
 
? Data Exploration means 
 
 
? Modelling means 
 
 
? Evaluation means 
 
 
17 
 
Ask students about possible solutions to this problem before moving ahead. 
Invite them to think of non-AI solutions as well. 
? Deployment means 
 
 
 
Let us understand! 
Let us go through the AI project cycle with the help of an example. 
 
Problem: Pest infestation damages crops 
The cotton industry in India consists of 6 million local farmers. Cotton crops frequently get infected with 
the Pink Bollworm. It is difficult to see these insects with the naked eye. Small farmers find it very difficult 
to get rid of these insects. They do not have advanced tools and techniques to protect their plants from 
Pink Bollworm. 
 
Can we solve this problem with AI? How? 
Watch the video at this link - https://www.youtube.com/watch?v=LP_A4jydmz4 
 
Now that you are aware of AI concepts, plan to use them in accomplishing your task. 
Start with listing down all the factors which you need to consider to save the cotton crop. 
This system aims to: 
 
 
 
18 
 
 
Now, as you interact with the farmers, you get to know different types of worms affecting 
the cotton crop. You will collect the following data 
? Images of the pest 
? Farmer names 
? Village names 
? Farm size 
? Pesticide usage 
 
After acquiring the required data, you realise that it is not uniform. Some images are small in size while 
others are big. Some images and other data are missing while you have multiple copies of others. So, we 
clean the data, try to make it uniform and fill in the missing data to make it more understandable. 
By exploring the data, researchers can identify patterns and trends related to Pink Bollworm infestations, 
pesticide usage, crop yields, and other relevant factors. 
 
After exploring the data, now you know that you need to develop an AI-enabled app using which the 
farmers will click the pictures of the collected pests using the phone camera. The AI app then decides 
whether the image is valid. Based on the number of pests recognized by the system and rules laid out by 
entomologists, recommendations are displayed 
 
 
Your pest management system is now complete! You test it by first emptying the trap of pests onto a 
blank sheet of paper and opening the app, then clicking pictures of pests. You notice that the results 
were 70% correct. After evaluating this model, you work on other shortlisted AI algorithms and work on 
them. 
You test the algorithms to 
 
 
19 
 
You can add ‘Small farms that used the app saw jumps in profit margins of up to 26.5 percent. 
A drop-in pesticide costs of up to 38 percent was also observed’. 
 
After proper testing, you deploy your pest management app by getting it installed on 
farmer’s mobile phones. 
 
 
Let us look at the main features of CottonAce app- 
CottonAce app 
? CottonAce is a mobile application that can help 
farmers protect their crops from pests. 
? CottonAce uses AI to warn the farmers about a 
possible pest infestation. 
? It aids farmers in – 
? Determining the correct amount of pesticides 
? Knowing the right time to spray pesticides 
? Seeking professional help as needed. 
 
How does it work? 
? A farmer sets up a trap to capture pests. 
? Take a picture of the captured pests. 
? Upload the picture on the app. 
? The app detects the insect, level of infestation, and 
the required measures to cure it. 
 
 
 
 
 
 
Page 5


16 
 
1.2 AI Project Cycle 
 
Lesson Title: AI Project Cycle Approach: Interactive Session 
Summary: Students will learn about the AI Project Cycle and get familiar with it. 
Learning Objectives: Students will know how they can get started on an AI project. 
Learning Outcomes: Describe the stages in the AI project cycle. 
Pre-requisites: Basic computer literacy 
Key-concepts: AI project cycle 
 
Let us think! 
? Problem Scoping means 
 
 
? Data Acquisition means 
 
 
? Data Exploration means 
 
 
? Modelling means 
 
 
? Evaluation means 
 
 
17 
 
Ask students about possible solutions to this problem before moving ahead. 
Invite them to think of non-AI solutions as well. 
? Deployment means 
 
 
 
Let us understand! 
Let us go through the AI project cycle with the help of an example. 
 
Problem: Pest infestation damages crops 
The cotton industry in India consists of 6 million local farmers. Cotton crops frequently get infected with 
the Pink Bollworm. It is difficult to see these insects with the naked eye. Small farmers find it very difficult 
to get rid of these insects. They do not have advanced tools and techniques to protect their plants from 
Pink Bollworm. 
 
Can we solve this problem with AI? How? 
Watch the video at this link - https://www.youtube.com/watch?v=LP_A4jydmz4 
 
Now that you are aware of AI concepts, plan to use them in accomplishing your task. 
Start with listing down all the factors which you need to consider to save the cotton crop. 
This system aims to: 
 
 
 
18 
 
 
Now, as you interact with the farmers, you get to know different types of worms affecting 
the cotton crop. You will collect the following data 
? Images of the pest 
? Farmer names 
? Village names 
? Farm size 
? Pesticide usage 
 
After acquiring the required data, you realise that it is not uniform. Some images are small in size while 
others are big. Some images and other data are missing while you have multiple copies of others. So, we 
clean the data, try to make it uniform and fill in the missing data to make it more understandable. 
By exploring the data, researchers can identify patterns and trends related to Pink Bollworm infestations, 
pesticide usage, crop yields, and other relevant factors. 
 
After exploring the data, now you know that you need to develop an AI-enabled app using which the 
farmers will click the pictures of the collected pests using the phone camera. The AI app then decides 
whether the image is valid. Based on the number of pests recognized by the system and rules laid out by 
entomologists, recommendations are displayed 
 
 
Your pest management system is now complete! You test it by first emptying the trap of pests onto a 
blank sheet of paper and opening the app, then clicking pictures of pests. You notice that the results 
were 70% correct. After evaluating this model, you work on other shortlisted AI algorithms and work on 
them. 
You test the algorithms to 
 
 
19 
 
You can add ‘Small farms that used the app saw jumps in profit margins of up to 26.5 percent. 
A drop-in pesticide costs of up to 38 percent was also observed’. 
 
After proper testing, you deploy your pest management app by getting it installed on 
farmer’s mobile phones. 
 
 
Let us look at the main features of CottonAce app- 
CottonAce app 
? CottonAce is a mobile application that can help 
farmers protect their crops from pests. 
? CottonAce uses AI to warn the farmers about a 
possible pest infestation. 
? It aids farmers in – 
? Determining the correct amount of pesticides 
? Knowing the right time to spray pesticides 
? Seeking professional help as needed. 
 
How does it work? 
? A farmer sets up a trap to capture pests. 
? Take a picture of the captured pests. 
? Upload the picture on the app. 
? The app detects the insect, level of infestation, and 
the required measures to cure it. 
 
 
 
 
 
 
20 
 
Conclusion: 
“Greater efficiency implies that the solution can be developed faster and in a more convenient way. Due 
to modularity, the complex problem of cotton diseases and the process of making a solution for it can be 
broken down into simpler steps”. 
What is AI project cycle mapping? 
Mapping the individual steps in an AI project to the steps in the AI project cycle. 
Let us map the steps of Pest Management project to the steps in the AI project cycle. 
 
 
 
Why do we need an AI Project Cycle?